Developing Novel Technology for Data Driven Clinical Decision Making

Glioblastoma (GBM) brain tumours are deadly and incurable. Effective treatment options for GBM are urgently needed. The extent of surgical GBM tumour resection substantially positively impacts patient survival, but damage to normal brain tissue can severely affect quality of life and hence must be minimised. Therefore, it is imperative that we develop tools for surgical removal which can:

A) precisely remove cancer cells, with reduced thermal impact on normal brain cells

B) give feedback at the time of resection to surgeons so that they know which areas of the brain tissue are malignant and need to be removed and

C) give doctors indications of the nature of the cancer so that the most effective therapy can be employed post-operatively for any cancer cells left behind.

Development of this technology would be instrumental to revolutionising the way surgeons treat cancer. To do this we will develop high-precision lasers and understand their effects on tissue using very high-definition cameras, we will then collect the 'smoke' produced by these lasers when cutting and feed it into a mass spectrometer which is capable of measuring all of the individual metabolite chemicals representative of the tissue it has come from.

When cancer is present, cells metabolise differently and hence this can be measured using the technology described. Observing the changes in metabolism can help us to understand complex diseases such as cancer and the therapies which may be effective against them. Therefore, in this project we will take the data from our novel technology and use machine learning to understand changes in the chemical compositions of the tissues and how that correlates to the different subtypes of cancer and use this information to predict the nature of any recurrent disease.

If we can predict the recurrent nature of disease from the primary tissue, we may be able to use therapies more effectively to stop recurrence altogether.